OBJECTIVE Absence status epilepticus (ASE) is a form of non-convulsive status epilepticus characterized by ongoing or intermittent epileptic activity accompanied by behavioral and cognitive changes. Herein, we assessed high-frequency oscillations in the ripple band in patients with ASE and typical absence seizures. METHODS We enrolled five patients with ASE, 26 patients with childhood absence epilepsy (CAE), and 15 patients with juvenile absence epilepsy (JAE). We performed time-frequency analysis of electroencephalogram data for ictal absence seizures at each electrode to assess the high frequency activity (HFA) rate, peak frequency, and peak power. RESULTS The average HFA rates were 60.7%, 20.8%, and 12.9% in ASE, CAE, and JAE patients, respectively. The average peak frequencies were 126.4&nbsp;Hz, 120.9&nbsp;Hz, and 126.1&nbsp;Hz in ASE, CAE, and JAE patients, respectively. The average peak power values were 2,388.8&nbsp;μV2, 120.9&nbsp;μV2, and 126.1&nbsp;μV2 in ASE, CAE, and JAE patients, respectively, and all epilepsy groups exhibited frontal-dominant ripple distribution. https://www.selleckchem.com/products/xmd8-92.html CONCLUSION ASE patients presented higher power and frontal dominant ripples of absence seizure, compared to CAE and JAE patients. SIGNIFICANCE Future studies should utilize scalp-recorded ripples as a biomarker of absence epilepsy. This may aid in the development of novel treatment strategies for ASE. OBJECTIVE Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals. METHODS A deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects. RESULTS In a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p&nbsp;=&nbsp;0.0075). CONCLUSIONS The MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL. SIGNIFICANCE This study validates a fully automatic method for scoring arousals in PSGs. OBJECTIVE The present investigation tested the association of a novel measure of brain activation recorded during a simple motor inhibition task with a GRM8 genetic locus implicated in risk for substance dependence. METHODS 122 European-American adults were genotyped at rs1361995 and evaluated against DSM-IV criteria for Alcohol Dependence, Cocaine Dependence, Conduct Disorder, and Antisocial Personality Disorder. Also, their brain activity was recorded in response to rare, so-called "No-Go" stimuli presented during a continuous performance test. Brain activity was quantified with two indices (1) the amplitude of the No-Go P300 electroencephalographic response averaged across trials; and (2) the inter-trial variability of the response. RESULTS The absence of the minor allele at the candidate locus was associated with all of the evaluated diagnoses. In comparison to minor allele carriers, major allele homozygotes also demonstrated increased inter-trial variability in No-Go P300 response amplitude but no difference in average amplitude. CONCLUSIONS GRM8 genotype is associated with Alcohol and Cocaine Dependence as well as personality risk factors for dependence. The association may be mediated through an inherited instability in brain function that affects cognitive control. SIGNIFICANCE The present study focuses on a metric and brain mechanism not typically considered or theorized in studies of patients with substance use disorders. OBJECTIVE To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy. METHODS We developed an algorithm for automatic detection of EDs, based on a novel deep learning method that requires a low amount of labeled EEG data for training. Detected EDs are automatically grouped into clusters, consisting of the same type of EDs, for rapid visual inspection. We validated the algorithm on an independent dataset of 100 patients with sharp transients in their EEG recordings (54 with epilepsy and 46 with non-epileptic paroxysmal events). The diagnostic gold standard was derived from the video-EEG recordings of the patients' habitual events. RESULTS The algorithm had a sensitivity of 89% for identifying EEGs with EDs recorded from patients with epilepsy, a specificity of 70%, and an overall accuracy of 80%. CONCLUSIONS Automated detection of EDs using an artificial intelligence-based computer algorithm had a high sensitivity. Human (expert) supervision is still necessary for confirming the clusters of detected EDs and for describing clinical correlations. Further studies on different patient populations will be needed to confirm our results. SIGNIFICANCE The automated algorithm we describe here is a useful tool, assisting neurophysiologist in rapid assessment of EEG recordings. BACKGROUND Prior controlled cannabis research has mostly focused on smoked cannabis and predominantly included frequent cannabis users. Oral cannabis products ("edibles") make up a large and growing segment of the retail cannabis market. This study sought to characterize the pharmacodynamic effects of oral cannabis among infrequent cannabis users. METHODS Seventeen healthy adults who had not used cannabis for at least 60 days completed four experimental sessions in which they consumed a cannabis-infused brownie that contained 0, 10, 25, or 50 mg THC. Subjective effects, vital signs, cognitive/psychomotor performance, and blood THC concentrations were assessed before and for 8 h after dosing. RESULTS Relative to placebo, the 10 mg THC dose produced discriminable subjective drug effects and elevated heart rate but did not alter cognitive/psychomotor performance. The 25 and 50 mg THC doses elicited pronounced subjective effects and markedly impaired cognitive and psychomotor functioning compared with placebo. For all active doses, pharmacodynamic effects did not manifest until 30-60 min after ingestion, and peak effects occurred 1.